{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "%matplotlib inline\n",
    "import cv2\n",
    "import pandas as pd\n",
    "import csv\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from util.dataset_util import *\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "def ellipse_detect(img):\n",
    "    \"\"\"\n",
    "    :param image: 图片路径\n",
    "    :return: None\n",
    "    \"\"\"\n",
    "    # img = cv2.imread(image,cv2.IMREAD_COLOR)\n",
    "    skinCrCbHist = np.zeros((256, 256), dtype= np.uint8 )\n",
    "    cv2.ellipse(skinCrCbHist ,(113,155),(23,15),43,0, 360, (255,255,255),-1)\n",
    " \n",
    "    YCRCB = cv2.cvtColor(img,cv2.COLOR_BGR2YCR_CB)\n",
    "    (y,cr,cb)= cv2.split(YCRCB)\n",
    "    skin = np.zeros(cr.shape, dtype=np.uint8)\n",
    "    (x,y)= cr.shape\n",
    "    for i in range(0,x):\n",
    "        for j in range(0,y):\n",
    "            CR= YCRCB[i,j,1]\n",
    "            CB= YCRCB[i,j,2]\n",
    "            if skinCrCbHist [CR,CB]>0:\n",
    "                skin[i,j]= 255\n",
    "    # cv2.namedWindow(image, cv2.WINDOW_NORMAL)\n",
    "    # cv2.imshow(image, img)\n",
    "    dst = cv2.bitwise_and(img,img,mask= skin)\n",
    "    return dst"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "source": [
    "def dataset_append(label, label_num, train_test, src):\n",
    "    video = cv2.VideoCapture(src)\n",
    "    # plt.figure()\n",
    "    cnt = 0\n",
    "    while True:\n",
    "        ret, img = video.read() # img 就是一帧图片\n",
    "        \n",
    "        if not ret: break # 当获取完最后一帧就结束\n",
    "        # img = img(cv2.Range(100, 1280-100), cv2.Range(100,720-100))\n",
    "        img = cv2.resize(img, (128,64))\n",
    "        # if train_test == 'test': img = img[100: 720-100,100:1280-100] \n",
    "        img = ellipse_detect(img)\n",
    "\n",
    "        # img = cv2.resize(img, (128,64))\n",
    "        img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "        # img = img.astype(np.float32)\n",
    "        img = cv2.Canny(img, 220, 255)\n",
    "        # _, canny = cv2.threshold(canny, 220, 255, cv2.THRESH_BINARY) \n",
    "        # plt.imshow(canny)\n",
    "        # plt.show()\n",
    "        # canny = canny.astype(np.float32)\n",
    "        # img/=256\n",
    "        # cv2.imshow('img', img)\n",
    "        # cv2.imshow('canny', canny)\n",
    "        # cv2.waitKey(10)\n",
    "        img_append(\"data/\"+train_test+\"_label.csv\", label, label_num, train_test, img)\n",
    "        cnt += 1\n",
    "    cv2.destroyAllWindows()\n",
    "    print(f'Add {train_test} {label} totally {cnt} images')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "source": [
    "csv_init(\"data/train_label.csv\")"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "source": [
    "#增加训练集\n",
    "labels = ['one', 'two', 'three']\n",
    "for i in range(len(labels)):\n",
    "    label = labels[i]\n",
    "    dataset_append(label, i, 'train', 'raw_data/train/'+label+'.mp4')"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Add train one totally 1376 images\n",
      "Add train two totally 1381 images\n",
      "Add train three totally 1398 images\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "source": [
    "csv_init(\"data/test_label.csv\")"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "source": [
    "#增加测试集\n",
    "for i in range(len(labels)):\n",
    "    label = labels[i]\n",
    "    dataset_append(label, i, 'test', 'raw_data/test/'+label+'.mp4')"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Add test one totally 197 images\n",
      "Add test two totally 185 images\n",
      "Add test three totally 173 images\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "source": [
    "#显示部分图片\n",
    "from CustomImageDataset import CustomImageDataset\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "# train_dataset = CustomImageDataset('data/train_label.csv', 'data/train')\n",
    "test_dataset = CustomImageDataset('data/test_label.csv', 'data/test')\n",
    "# train_dataloader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n",
    "test_dataloader = DataLoader(test_dataset, batch_size=32)\n",
    "\n",
    "# print(len(test_dataset))\n",
    "\n",
    "plt.figure(figsize=(15,6))\n",
    "plt.suptitle('examples dataset', color='w', fontsize=32)\n",
    "for i in range(10):\n",
    "    plt.subplot(2,5,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    x, y = test_dataloader.dataset[int(i*(len(test_dataset)/10))]\n",
    "    plt.imshow(x[0])\n",
    "    x = x.unsqueeze(0).cuda().float()\n",
    "    # predict = torch.nn.functional.softmax(model(x), dim = 1).argmax().item()\n",
    "\n",
    "    label_str = f'GT: {labels[y]}'\n",
    "    plt.xlabel(label_str, color='w', fontsize=16)\n",
    "plt.show()\n"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1080x432 with 10 Axes>"
      ],
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"
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  }
 ],
 "metadata": {
  "orig_nbformat": 4,
  "language_info": {
   "name": "python",
   "version": "3.9.6",
   "mimetype": "text/x-python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "pygments_lexer": "ipython3",
   "nbconvert_exporter": "python",
   "file_extension": ".py"
  },
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.9.6 64-bit ('ys': conda)"
  },
  "interpreter": {
   "hash": "d015276d552ef9c4c4403205f34125a6e79e389b6695ae5dca85ba2d9ee62411"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}